La educación en la sociedad del conocimiento se enfrenta a enormes retos que deben ser afrontados bajo la perspectiva de la innovación pedagógica y didáctica. En esta época de cambios el Aprendizaje Basado en Desafíos se erige como una orientación oportuna para el mejoramiento de las prácticas de enseñanza y aprendizaje, particularmente en los programas de administración de empresas ofertados en instituciones de educación superior. Si bien el CBL es una metodología que en los últimos años se ha venido desarrollando en distintas áreas del conocimiento, en las ciencias económicas y administrativas aún se desconoce todo el potencial que este enfoque tiene para ofrecer a la formación universitaria. Siendo esto así, este artículo presenta una revisión sistemática de la literatura sobre el aporte pedagógico del Challenge-Based Learning a la formación de nuevos administradores de empresas en la educación superior, de tal forma que puedan responder a las necesidades de las organizaciones del presente y del futuro. Se realiza un estudio descriptivo analizando las investigaciones publicadas desde el año 2019 hasta el 2023, siguiendo las directrices Prisma. Se revisaron 82 artículos utilizando una matriz temática cualitativa. Los resultados describen los beneficios de la integración del aprendizaje basado en desafíos en la estructura pedagógica de los programas de administración de empresas. Con esta investigación se aporta al estado del arte de un aspecto relevante que se abre paso en la educación superior en América Latina.

The global palm oil industry faces mounting pressure to balance economic growth with environmental protection and social equity. This study addresses the critical need for sustainable and resilient supply chain management (SCM) strategies in response to deforestation concerns, regulatory pressures, and stakeholder demands. The purpose is to develop an integrative conceptual framework that unites sustainability imperatives, digital transformations, and multi-stakeholder engagement in palm oil SCM. Through a systematic literature review of 46 peer-reviewed articles from Scopus and Web of Science, complemented by bibliometric analysis, this research consolidates existing knowledge on SCM practices, emergent technologies, and policy instruments. Findings indicate that integrating blockchain, machine learning, and circular economy principles significantly enhances traceability, resource efficiency, and compliance with sustainability certifications such as RSPO and NDPE. However, persistent barriers including high implementation costs, limited infrastructure, and smallholder exclusion hinder largescale adoption. The proposed framework emphasizes the interdependence of regulatory frameworks, technological enablers, circular economy strategies, and policy alignment. Critical research gaps identified include the need for longitudinal analyses of certification impacts, comparative studies beyond Southeast Asia, and deeper investigations into social sustainability. This study provides actionable guidelines for industry practitioners and policymakers to foster a more sustainable, transparent, and resilient palm oil industry

Air pollution is a major environmental risk to public health, strongly associated with cardiovascular and respiratory diseases. According to the World Health Organization, at least 99% of the global population lives in areas that do not meet its air-quality guidelines. PM $$_{2.5}$$ 2.5 variability reflects the interaction between local emission sources and meteorological processes that regulate near-surface dispersion and accumulation, producing nonlinear temporal patterns that motivate hybrid modeling approaches. We propose an SSA-SVR-BiLSTM framework for short-term PM $$_{2.5}$$ 2.5 forecasting that combines SSA-optimized SVR and BiLSTM models within a constrained linear ensemble with weights estimated exclusively on the validation partition using Lagrange multipliers. The model is evaluated on 10 months of high-frequency (5-min) air-quality and meteorological data collected at a traffic-influenced monitoring site in Duitama, Colombia. On the held-out test set, the hybrid achieved $$R^2 = 0.9585$$ R 2 = 0.9585 and MAPE $$= 7.62\%$$ = 7.62 % , outperforming five of six SSA-optimized baselines; the difference relative to the best individual model (SSA-SVR was statistically significant but practically negligible ( $$p = 0.0105$$ p = 0.0105 , $$\Delta R^2 = 0.0002$$ Δ R 2 = 0.0002 ), and the value of the hybrid lies primarily in its improved consistency across low and medium concentration regimes rather than in a large global accuracy gain. Stratified evaluation across concentration regimes and normative thresholds (WHO, EPA) revealed systematic underestimation at extreme pollution episodes, a structural limitation shared by all evaluated models. A feature occlusion analysis identified PM $$_5$$ 5 and PM $$_{10}$$ 10 as the dominant exogenous predictors (53.6 and 21.7%), with meteorological variables contributing an additional $${\sim }20\%$$ ∼ 20 % of the total relevance.